In recent years, there have been great advances in the field of machine learning. Much of these advances have been in machine trained networks (e.g., deep neural networks) and algorithms for training such networks. However, there has not been as much advances in circuits for implementing machine-trained networks. This has been primarily due to an over reliance on implementing machine trained networks in datacenters as opposed to in devices in the real world. Therefore, there is a need in the art for innovative circuits for implementing machine trained networks as well as other types of designs.